Image-Text-to-Text
Transformers
Safetensors
MLX
qwen2_5_vl
medical
multimodal
report generation
radiology
clinical-reasoning
MRI
CT
Histopathology
X-ray
Fundus
mlx-my-repo
conversational
text-generation-inference
Instructions to use introvoyz041/Lingshu-7B-mlx-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use introvoyz041/Lingshu-7B-mlx-fp16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="introvoyz041/Lingshu-7B-mlx-fp16") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("introvoyz041/Lingshu-7B-mlx-fp16") model = AutoModelForMultimodalLM.from_pretrained("introvoyz041/Lingshu-7B-mlx-fp16") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - MLX
How to use introvoyz041/Lingshu-7B-mlx-fp16 with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("introvoyz041/Lingshu-7B-mlx-fp16") config = load_config("introvoyz041/Lingshu-7B-mlx-fp16") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- vLLM
How to use introvoyz041/Lingshu-7B-mlx-fp16 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "introvoyz041/Lingshu-7B-mlx-fp16" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "introvoyz041/Lingshu-7B-mlx-fp16", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/introvoyz041/Lingshu-7B-mlx-fp16
- SGLang
How to use introvoyz041/Lingshu-7B-mlx-fp16 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "introvoyz041/Lingshu-7B-mlx-fp16" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "introvoyz041/Lingshu-7B-mlx-fp16", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "introvoyz041/Lingshu-7B-mlx-fp16" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "introvoyz041/Lingshu-7B-mlx-fp16", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use introvoyz041/Lingshu-7B-mlx-fp16 with Docker Model Runner:
docker model run hf.co/introvoyz041/Lingshu-7B-mlx-fp16
| license: mit | |
| library_name: transformers | |
| pipeline_tag: image-text-to-text | |
| tags: | |
| - medical | |
| - multimodal | |
| - report generation | |
| - radiology | |
| - clinical-reasoning | |
| - MRI | |
| - CT | |
| - Histopathology | |
| - X-ray | |
| - Fundus | |
| - mlx | |
| - mlx-my-repo | |
| base_model: lingshu-medical-mllm/Lingshu-7B | |
| # introvoyz041/Lingshu-7B-mlx-fp16 | |
| The Model [introvoyz041/Lingshu-7B-mlx-fp16](https://huggingface.co/introvoyz041/Lingshu-7B-mlx-fp16) was converted to MLX format from [lingshu-medical-mllm/Lingshu-7B](https://huggingface.co/lingshu-medical-mllm/Lingshu-7B) using mlx-lm version **0.28.3**. | |
| ## Use with mlx | |
| ```bash | |
| pip install mlx-lm | |
| ``` | |
| ```python | |
| from mlx_lm import load, generate | |
| model, tokenizer = load("introvoyz041/Lingshu-7B-mlx-fp16") | |
| prompt="hello" | |
| if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: | |
| messages = [{"role": "user", "content": prompt}] | |
| prompt = tokenizer.apply_chat_template( | |
| messages, tokenize=False, add_generation_prompt=True | |
| ) | |
| response = generate(model, tokenizer, prompt=prompt, verbose=True) | |
| ``` | |